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Reinforcement Learning and Decision Making: Neural and Computational Mechanisms


Entry requirements

Open to MSc Psychology (research) students.


Reinforcement learning is the adaptive process by which agents use their previous experiences to predict and optimize the consequences of their behavior. Over the last few decades, this has been intensively studied in a range of fields including psychology, artificial intelligence, animal and human neuroscience and economics. A related but largely separate literature is concerned with how agents detect, and make optimal decisions about, noisy sensory information. Computational models of the putative underlying mechanisms of learning and decision making play a central role in both of these research fields. This course is intended to provide an overview of the psychological, computational and neural mechanisms of (i) value-based reinforcement learning and decision-making and (ii) perceptual decision-making, and to discuss underlying commonalities between these research fields. Topics include Markov decision processes, the exploitation-exploration tradeoff, and sequential sampling models of perceptual decision-making.

The course meetings will be based on empirical papers that have made a significant contribution to the field and on papers that review a substantial body of research. Each student will write a short essay about the relationship between reinforcement learning and decision making, and will initiate and lead a group discussion about this. In addition, students will gain hands-on experience with fitting computational models to experimental data, apply a model-based analysis to a data set, and present their results on a scientific poster.

Course objectives Upon completion of the course, students will: - Be introduced to the fields of reinforcement learning and decision-making, and obtain an overview of their key paradigms, models, findings and challenges;

  • Gain insight and practical experience in the use of computational models to explain and interpret experimental data; and

  • Obtain a basic understanding in how to translate abstract theoretical concepts in the field to concrete experiments, and learn to account for experimental findings through modelling considerations.


For the timetables of your lectures, work groups and exams, please select your study programme in:
Psychology timetables



Students need to enroll for lectures and work group sessions.
Master’s course registration


Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date. Students who are not registered will not be permitted to take the examination.
Registering for exams

Mode of instruction

8 seminars (meetings) of 2 hours

Assessment method

The assessment of the course is based on:

  • 50% short essay and related discussion

  • 50% poster and poster presentation

The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.

Reading list

  • Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1481), 933-942.

  • Dayan, P., & Daw, N. D. (2008). Decision theory, reinforcement learning, and the brain. Cognitive, Affective & Behavioral Neuroscience, 8(4), 429-453.

  • Summerfield, C., & Tsetsos, K. (2012). Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms. Frontiers in Neuroscience, 6, 70.

Contact information

Dr. Marieke Jepma